The monitoring and maintenance of the Inner Mongolia section of the Yellow River Basin is of great significance to the safety and development of China’s Yellow River Economic Belt and to the protection of the Yellow River ecology. In this study, we calculated diagnostic values from a total of 520 Landsat OLI/TM remote sensing images of the Yellow River Basin of Inner Mongolia from 2001 to 2020. Using the RSEI and the GEE Cloud Computing Jigsaw, we analyzed the spatial and temporal distribution of diagnostic values representative of the basin’s ecological status. Further, Mantel and Pearson correlations were used to analyze the significance of environmental factors in affecting the ecological quality of cities along the Yellow River within the study area. The results indicated that the overall mean of RSEI values rose at first and then fell. The RSEI grade to land area ratio was calculated to be highest in 2015 (excellent) and worst in 2001. From 2001 to 2020, ecological quality monitoring process of main cities in the Inner Mongolia region of the Yellow River Basin. Hohhot, Baotou, and Linhe all have an RSEI score greater than 0.5, considered average. However, Dongsheng had its best score (0.60, good) in 2005, which then declined and increased to an average rating in 2020. The RSEI value for Wuhai reached excellent in 2010 but then became poor in 2020, dropping to 0.28. The analysis of ecological quality in the city shows that the greenness index (NDVI) carried the most significant impact on the ecological environment, followed by the humidity index (Wet), the dryness index (NDBSI), the temperature index (Lst), land use, and then regional gross product (RGP). The significance of this study is to provide a real-time, accurate, and rapid understanding of trends in the spatial and temporal distribution of ecological and environmental quality along the Yellow River, thereby providing a theoretical basis and technical support for ecological and environmental protection and high-quality development of the Yellow River Basin.

Forests are the most important part of terrestrial ecosystems. In the context of China’s industrialization and urbanization, mining activities have caused huge damage to the forest ecology. In the Ulan Mulun River Basin (Ordos, China), afforestation is standard method for reclamation of coal mine degraded land. In order to understand, manage and utilize forests, it is necessary to collect local mining area’s tree information. This paper proposed an improved Faster R-CNN model to identify individual trees. There were three major improved parts in this model. First, the model applied supervised multi-policy data augmentation (DA) to address the unmanned aerial vehicle (UAV) sample label size imbalance phenomenon. Second, we proposed Dense Enhance Feature Pyramid Network (DE-FPN) to improve the detection accuracy of small sample. Third, we modified the state-of-the-art Alpha Intersection over Union (Alpha-IoU) loss function. In the regression stage, this part effectively improved the bounding box accuracy. Compared with the original model, the improved model had the faster effect and higher accuracy. The result shows that the data augmentation strategy increased AP by 1.26%, DE-FPN increased AP by 2.82%, and the improved Alpha-IoU increased AP by 2.60%. Compared with popular target detection algorithms, our improved Faster R-CNN algorithm had the highest accuracy for tree detection in mining areas. AP was 89.89%. It also had a good generalization, and it can accurately identify trees in a complex background. Our algorithm detected correct trees accounted for 91.61%. In the surrounding area of coal mines, the higher the stand density is, the smaller the remote sensing index value is. Remote sensing indices included Green Leaf Index (GLI), Red Green Blue Vegetation Index (RGBVI), Visible Atmospheric Resistance Index (VARI), and Normalized Green Red Difference Index (NGRDI). In the drone zone, the western area of Bulianta Coal Mine (Area A) had the highest stand density, which was 203.95 trees ha⁻¹. GLI mean value was 0.09, RGBVI mean value was 0.17, VARI mean value was 0.04, and NGRDI mean value was 0.04. The southern area of Bulianta Coal Mine (Area D) was 105.09 trees ha⁻¹ of stand density. Four remote sensing indices were all the highest. GLI mean value was 0.15, RGBVI mean value was 0.43, VARI mean value was 0.12, and NGRDI mean value was 0.09. This study provided a sustainable development theoretical guidance for the Ulan Mulun River Basin. It is crucial information for local ecological environment and economic development.

In remote sensing, active learning (AL) is considered to be an effective solution to the problem of producing sufficient classification accuracy with a limited number of training samples. Though this field has been extensively studied, most papers exist in the pixel-based paradigm. In object-based image analysis (OBIA), AL has been comparatively less studied. This paper aims to propose a new AL method for selecting object-based samples. The proposed AL method solves the problem of how to identify the most informative segment-samples so that classification performance can be optimized. The advantage of this algorithm is that informativeness can be estimated by using various object-based features. The new approach has three key steps. First, a series of one-against-one binary random forest (RF) classifiers are initialized by using a small initial training set. This strategy allows for the estimation of the classification uncertainty in great detail. Second, each tested sample is processed by using the binary RFs, and a classification uncertainty value that can reflect informativeness is derived. Third, the samples with high uncertainty values are selected and then labeled by a supervisor. They are subsequently added into the training set, based on which the binary RFs are re-trained for the next iteration. The whole procedure is iterated until a stopping criterion is met. To validate the proposed method, three pairs of multi-spectral remote sensing images with different landscape patterns were used in this experiment. The results indicate that the proposed method can outperform other state-of-the-art AL methods. To be more specific, the highest overall accuracies for the three datasets were all obtained by using the proposed AL method, and the values were 88.32%, 85.77%, and 93.12% for “T1,” “T2,” and “T3,” respectively. Furthermore, since object-based features have a serious impact on the performance of AL, eight combinations of four feature types are investigated. The results show that the best feature combination is different for the three datasets due to the variation of the feature separability.